Analysis And Optimization Mechanism Of Memory Access Model In Virtualized Environment

The traditional data center is built on multiple entity servers to accomplish various tasks in parallel.Once the task increases,the number of servers and investment also needs to be increased.To solve the problem of high cost and low resource utilization,virtualization technology is emerged.By using virtualization technology,less server resources can run multiple applications and different operating systems,and can be used by multiple users at the same time,which reduces the cost of deploying servers and increasing productivity.In this paper,a new memory access architecture and model are proposed by analyzing and studying the memory access model in virtualized environment.On this basis,a page replacement algorithm based on GPU acceleration is designed.The spare computing power of GPU is utilized to perform high-speed replacement and compression of memory replacement data,which effectively improves the utilization of memory resources.The main research work of this paper is as follows:(1)The memory access data in virtualized environment is analyzed,designed and modeled.Firstly,the Docker and K8S(Kubernetes)architectures are used to build the virtualization service environment.Then,the stress test is employed to simulate a large number of user access.Finally,the data of memory access is collected through experiments,and the Zipf index and the Gini coefficient are applied to determine whether it conforms to the Zipf's law.According to the visualization of the experimental results,it can be seen that the data are distributed in a stepped way.Therefore,Ladder Aggregation Distribution(LAD)which is a new memory access model is constructed for the virtualized environment.The experimental results demonstrate that the distribution of LAD is more in line with the actual situation.(2)The page replacement algorithm based on GPU acceleration is optimized and designed.To solve the LAD distribution problem of memory access in virtualized environment established by Docker,this paper designs a page replacement algorithm based on GPU acceleration.The page replacement algorithm based on GPU acceleration mainly involves in GPU spare computing power exploration,and GPU internal data fast index search and compression.The exploration of free computing power of GPU aims to make reasonable use of free computing power of GPU.Fast index search and compression of data in GPU is used to determine whether the required data is in GPU and to compress the data in GPU.Firstly,the data with low frequency in memory is put into GPU,and the data in GPU is compressed by GPU computing power.Then,when the missing page interruption occurred on the CPU,the data will be uncompressed in the GPU to the CPU.Finally,the compression performance,page replacement delay,hit ratio and memory utilization of GPU-based page replacement algorithm are analyzed and compared.